2020/12/07 by Anoushka Vyas, Vyas, Anoushka, Sambaran Bandyopadhyay +1 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Computational Physics and Python Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Social and Information Networks (cs.SI) #cs.AI #cs.LG #cs.SI
paper · pdf · doi:10.48550/arxiv.2012.03506
Accepted for publication in IJCAI 2022
openalex publication_date 2020/12/07 · arxiv created 2022/05/16 · arxiv updated 2022/05/17 · openalex created_date 2022/11/04 · openalex updated_date 2026/07/28
Soil moisture is an important component of precision agriculture as it directly impacts the growth and quality of vegetation. Forecasting soil moisture is essential to schedule the irrigation and optimize the use of water. Physics based soil moisture models need rich features and heavy computation which is not scalable. In recent literature, conventional machine learning models have been applied for this problem. These models are fast and simple, but they often fail to capture the spatio-temporal correlation that soil moisture exhibits over a region. In this work, we propose a novel graph neural network based solution that learns temporal graph structures and forecast soil moisture in an end-to-end framework. Our solution is able to handle the problem of missing ground truth soil moisture which is common in practice. We show the merit of our algorithm on real-world soil moisture data.